Multi-user adaptive array receiver and method

ABSTRACT

An array receiver which reduces complexity and cost by exploiting multiuser information in signals received from a plurality of transmitting users preprocesses ( 40 ) samples of antenna signals ( x 1 , x 2  . . . , x N ) from the antenna elements ( 22/1, . . . , 22 /N) to form basis signals (y O  . . . , y M ) together having fewer space-time dimensions than the space-time dimensions of the combined antenna signals. The receiver processes and combines the basis signals to produce sets of estimated received signals (z 0 , . . . , z M ), each for a corresponding one of the users. Each of the basis signals comprises a different combination of the antenna signals. The receiver combines the basis signals to provide a user-specific output signal, and periodically updates parameters of the filters ( 40/0, . . . , 40 /M) used for deriving each particular basis signal such that each user-specific output signal will exhibit a desired optimized concentration of energy of that user&#39;s received signal as received by the array antenna.

TECHNICAL FIELD

The invention relates to a receiver system comprising an antenna and areceiver, the antenna comprising an array of antenna elements. It alsorelates to the receiver per se and to a receiving method. The inventionis especially, but not exclusively, applicable to array receivers andmethods for use in base stations of digital cellular telecommunicationsnetworks or access points of wireless local area networks (LANs).

BACKGROUND ART

The invention is applicable in systems wherein multiple userssimultaneously make use of a common carrier or use distinct carrierswith some amount of overlap/crosstalk between them such as (i) carrierreuse-within-cell (RWC), also called Space-Division Multiple Access(SDMA) because of the need for an antenna array to spatiallydiscriminate co-channel signals; (ii) Code-Division Multiple Access(CDMA) systems where multiple users transmit in the same band usingdistinct codes; and (iii) Time-Division Multiple Access (TDMA) and/orFrequency-Division Multiple Access (FDMA) systems where users are notperfectly separable in time and/or frequency, i.e. they interfere withone another either in time (e.g. because of dispersive channels) and/orin frequency (e.g. because of excess bandwidth due to imperfect channelfiltering) thus leading to adjacent-channel interference (ACI).

Mathematical expressions in this patent specification are based uponcomplex baseband notation.

Array antenna radio receivers typically are employed at the basestations or access points of digital communications systems (e.g. mobiletelephone networks, broadband wireless access for Internet and/orwide-area networking, etc.) to improve reception link quality (i.eprovide robustness against multipath fading) and/or reduce interferencelevels, where interference can include thermal noise and man-madesignals which exist in the desired signal's band. Since such systemstypically accommodate large numbers of simultaneously active users inany given cell or sector, the base station receiver must be capable ofmaintaining a plurality of radio links.

Known antenna array radio receiver systems comprise an array of antennaelements coupled to a signal receiving section (also referred to as aradio-frequency (RF) front-end) which in turn is coupled to a signalprocessing section. The signal receiving section processes the branchsignals from the different antenna elements independently, in separatebranches, and performs on each branch signal standard downconversion,demodulation, filtering to isolate the channel of interest and,possibly, some transformation on the signal to bring it to a form usableby the signal processing section (e.g. analog-to-digital conversion ifthe signal processor is digital). The signal processor takes theinformation from all of the branches (i.e. the demodulated, filtered andsuitably transformed signal data from each individual antenna element)and, using one of a number of appropriate known techniques, combines andprocesses it to extract a useful signal y(t), which is the best possibleestimate of the desired user signal s₀(t).

In the context of wireless communications, the received vector x(t)(i.e. the received signal across all array elements) is made up of adesired signal s₀(t) transmitted by a “desired user's” wirelessterminal, interfering signals s_(m)(t) transmitted by competingterminals which operate in the same frequency band or in adjacent bandswith some amount of crosstalk being present, and white noise n(t).Hence, in non-dispersive (i.e. narrowband) environments $\begin{matrix}{{{x(t)} = {{{c_{0}(t)}{s_{0}(t)}} + {\sum\limits_{m = 1}^{M}{{c_{m}(t)}{s_{m}(t)}}} + {n(t)}}},} & (1)\end{matrix}$where c_(m)(t) is an N×1 vector of complex elements describing thechannel from the mth terminal to all of the N array elements, M is thenumber of interfering signals, n(t) is the white thermal noise vector,and c₀(t) is an N×1 complex vector describing the channel from the 0thterminal which, by convention, is that of the desired user.

In such a context, the function of the antenna array radio receiver isto isolate the desired user signal s₀(t) from the interferers and whitenoise as well as compensate for distortions introduced in the channelc₀(t) (e.g. multipath fading) so that, at all times, the array outputsignal y(t) approximates the desired user signal s₀(t) as closely aspossible.

Typically, the receiver combines the branch signals from the individualantenna elements simply by means of a linear weight-and-sum operation.If an N-element array is considered and x(t) is the N×1 vector of thearray element outputs, the array output is defined asy(t)=w^(H)(t)x(t),  (2)where w(t) is the N×1 complex weight vector and (•)^(H) denotes theHermitian transpose (i.e. complex conjugate transpose of its argument,be it a vector (as it is in the above) or a matrix).

Although it is time-varying, the weight vector varies slowly compared tothe input and output signals, since it tracks changes in the channels,not in the signals themselves. When a combiner operates according toequation (2), it is termed a linear combiner and the entire receiver isdesignated a linear array receiver.

Typically, the receiver collects statistics of the input signal x(t) anduses them to derive a weight vector which minimizes some error measurebetween the array output y(t) and the desired signal s₀(t). One of themost common error measures in such applications (i.e. adaptivefiltering) is the mean-square errorε=<[y(t)−s ₀(t)]² >=<[w ^(H)(t)x(t)−s ₀(t)]²>,   (3)which forms an N-dimensional quadratic surface with respect to theweight vector elements. The minimization of this criterion forms thebasis of minimum mean-square error (MMSE) linear array receivers (alsocalled optimum combiners).

(Note: Henceforth, the dependence upon time t in equations will beomitted for the sake of clarity.)

Adaptive filtering theory indicates that the best combination of weightsin the MMSE for a given sequence of received data isw=R_(xx) ⁻¹c₀  (4)where R_(xx) is the covariance matrix of the received array outputs andis given byR_(xx)=<xx^(H)>,  (5)where (•) denotes the expectation (i.e. the ensemble average) of itsargument.

Such array receivers are suitable for use where tine dispersion due tomultipath propagation does not extend significantly beyond a singlesymbol period. That is, there is little or no intersymbol interference(ISI).

When the channels carrying useful signals do exhibit significant ISI,the traditional solution is to use an equalizer, which is an adaptivefilter whose purpose is to invert the channel impulse response (thusuntangling the ISI) so that the overall impulse response at its outputwill tend to be much shorter in time and have an ideal, flat (orequalized) frequency spectrum.

The signal processing portion of the standard linear equalizer works inthe same way as a linear adaptive array receiver except that the signalsources, i.e., the elements of the input vector x, are not points inspace (i.e. the array of antenna elements) but points in time. Thesignals are tapped at a series of points along a symbol-spaced delayline (termed a tapped-delay line or TDL), then weighted and combined.

While the implementation of the signal processing apparatus for both theequalizer and the array receiver can be identical (minimization of theMSE by adaptive weighting of the inputs), the performance will differ.Because signals are physically sampled at different points in space bythe array receiver, it is very effective at nulling unwanted signalsources or co-channel interference (CCI). However, it has limitedability against intersymbol interference (ISI) due to dispersive, i.e.frequency-selective, fading, since the latter is spread in time. On theother hand, the equalizer is adept at combatting ISI but has limitedability against CCI.

In environments where both ISI and CCI are present, array reception andequalization may be combined to form a space-time array receiver. Themost general form of the latter is obtained when each weightingmultiplier in a narrow band array receiver is replaced by a fullequalizer for a total of N equalizers. Again the implementation of thesignal processing section will be similar and will rely on equation (2)supra. The only difference is that the weight vector w and the inputvector x will each be longer. Indeed, for an equalizer length of L tapsand an array size of N elements, the vectors w and x will both have LNelements.

The canonical linear mean-square-error minimizing space-time receiver(i.e. the most obvious and immediate linear space-time receiverstructure and also in certain respects the most complex) comprises anantenna array where each array element output is piped to a finiteimpulse response (FIR) adaptive filter, which in this context isreferred to as an equalizer. Each adaptive filter comprises atapped-delay line where taps are spaced by a symbol period or a fractionof a symbol period. For good performance, the length of the tapped-delayline should be equal or superior to the average channel memory length.In many cases, the number of taps this implies can be very large (e.g.10-100 per adaptive filter).

The weights multiplying each tap output must be constantly adapted tofollow the changes in the channel(s) characteristics. This can beperformed in various ways, either with continuous or block-basedadaptation and with or without the support of known training symbols. Inmost known systems, the weights are computed on a block-by-block basis(block adaptation) and each block contains a sequence of known trainingsymbols for that purpose. In digital wireless communications, the blockused for adaptation purposes will typically correspond to a data packetas defined by the networking protocol in use. Moreover, the channels canbe considered static over the length of a block (i.e., the length of ablock is significantly smaller than the channel correlation time).

By adapting the weights to minimize a global performance index, e.g. themean-square error between the desired signal and the S-T receiveroutput, the receiver usually performs the following:

-   -   reduces or eliminates intersymbol interference (ISI) caused by        frequency-selective fading in wideband channels;    -   reduces or eliminates co-channel interference (CCI) from nearest        cells where carriers are reused or from inside the cell (since        the space-time processor permits reuse of carriers within        cell—or sector—thanks to its power of spatial        discrimination—often referred to as space division        multiple-access (SDMA));    -   improves output SNR (due to the array's larger effective        aperture).

Since wireless systems are typically interference-limited (i.e.,interference is the main impediment which prevents capacityincrease—accommodating more active users—above a certain limit), thefirst two benefits of space-time processors are mainly of interest inorder to increase capacity.

To achieve maximal benefit, it is better to combine the S-T array withcarrier reuse-within-cell (RWC). A number of previous patents disclosearrays (see, for example U.S. Pat. Nos. 5,515,378 and 5,592,490) orspace-time systems (see, for example, U.S. Pat. No. 5,828,658) appliedin an SDMA (i.e. RWC) context. In such a system, separate S-T processorswill have to be implemented for every user (all processors share thesame physical antenna array and front-end receiver circuitry but havedistinct equalizers and combiners). However, the base station hasinformation (received symbols, channel characteristics) available aboutin-cell interferers since each in-cell interferer is another local S-Tprocessor's desired signal.

S-T processor architectures can be formulated to exploit this multiuserinformation by establishing some type of connectivity between individualS-T processors to achieve one of two goals:

-   -   improve performance (reduced bit-error rate, improved        interference nulling, etc.);    -   reduce complexity and cost.

It is known to exploit multiuser information to perform “jointdetection” of many users, for example by constructing a global multiuserMSE criterion, thus improving performance of an array receiver (withrespect to single user detection) at the cost of increased complexity[2], [3].

It is also known that, with appropriate space-time processing, it ispossible to combine SDMA with adequate temporal processing to mitigatethe intersymbol interference (ISI) present in wideband dispersivechannels [6].

One of the main disadvantages of previously-known space-time processingreceivers is their great complexity and cost, especially if multiuserdetection is employed and/or temporal processing employed.

It is known to reduce bandwidth requirements in forward-channel probingtransmitters by tracking only long-term variations in the channels(i.e., the subspace structure) [11] but that approach is not applicablein receivers without seriously limiting user capacity.

DISCLOSURE OF INVENTION

The present invention seeks to at least mitigate the disadvantages ofsuch known array receiver systems and to this end provides a multiuserspace-time array receiver, and system incorporating same, exploitingmultiuser information in order to reduce complexity and cost.

According to one aspect of the present invention, there is provided anarray receiver for processing signals received from a plurality oftransmitting users via an array antenna having an array of N antennaelements providing a set of antenna signals (x₁, x₂, . . . , x_(N)),respectively, each comprising information from each user, characterizedby a common preprocessing section for sampling each of the antennaelement signals (x₁, x₂, . . . , x_(N)) and processing the samples of atleast some of said antenna element signals to form a plurality ofsubspace basis signals, for example subspace signals, (y₀, . . . ,y_(M)) together having fewer space-time dimensions than the space-timedimensions of the combined antenna signals, and a plurality of signalprocessing units each having a plurality of inputs coupled to the commonpreprocessing unit for receiving all of the basis signals, eachprocessing unit processing and combining said basis signals to produce arespective one of a set of estimated received signals (z₀, . . . ,z_(M)) each for a corresponding one of the users, the commonpreprocessing section comprising filtering means for combining all ofthe antenna signals (x₁, x₂, . . . , x_(N)) to provide said plurality ofbasis signals (y₀, . . . , y_(M)), each of the basis signals comprisinga different combination of the antenna signals, each of the signalprocessing units combining the basis signals to provide a user-specificoutput signal, and updating means for periodically updating parametersof the filtering means used for deriving each particular basis signalsuch that each user-specific output signal will exhibit a desiredoptimized concentration of energy of that user's received signal asreceived by the array antenna.

In preferred embodiments, the updating means comprises means foradjusting said parameters in dependence upon channel characteristics ofall user channels. Each of the processor units then may comprise meansfor weighting the basis signals before combining same, the weights beingadjusted in dependence upon channel characteristics of all userchannels, wherein the parameters of the filtering means are updated lessfrequently than the weights of the processors.

The number of basis signals may be equal to the number of desired users.

The updating means may comprise a training sequence generator forgenerating a training sequence for the corresponding user, covariancematrix estimation means responsive to the training sequence and theantenna signals for providing a covariance matrix embodying long-termstatistics for the channel of that user, and eigenvector estimationmeans for extracting from said covariance matrix at least the dominanteigenvector, elements of said dominant eigenvector being applied to saidfiltering means as weights for updating said parameters.

Preferred embodiments of the first aspect of the present inventionaddress the complexity issue by (1) creating, from the antenna elementoutputs, a common basis of filters (i.e. filter bank) useful for allusers' processors, (2) adapting this basis based on the slowly-varyingstatistical channel structure, thus reducing the computational burden,and (3) by selecting for each user only a few (e.g. 2 or 3) mostsignificant filter outputs from the common basis for rapid adaptation.

Preferably, when the array receiver system is employed in aspace-division multiple access (SDMA) communications system, theplurality of basis filters in the preprocessing unit and the pluralityof subsequent receiver signal processing units are both assigned to anensemble of transmitting users sharing a common channel (i.e. frequencyband) at the same time.

Alternatively, the plurality of basis filters and subsequent receiversections could be assigned to an ensemble of transmitting antennasbelonging to the same user, yet transmitting different bit sequences inorder to provide a higher aggregate bit rate. This latter configurationcorresponds to a multi-input (MIMO) link. It should be understood that,in the following, references to a “user” in a SDMA context wouldtranslate to “transmitting antenna” in a MIMO context and that thetechniques described in a SDMA context otherwise are directly applicablein a MIMO context.

According to a second aspect of the invention, there is provided areceiver for receiving signals from a plurality of transmitting usersvia an array antenna having an array of N antenna elements providing aset of antenna signals (x₁, x₂, . . . x_(N)), respectively, eachcomprising information from each user, said receiver characterized by acommon preprocessing section followed by a plurality of receiversections, each corresponding to a different one of the users and coupledto the outputs of the common preprocessing section, the preprocessingsection sampling each of the antenna signals (x₁, x₂, . . . , x_(N)) andprocessing the samples of at least some of said antenna element signalsto form a plurality of basis signals (y₀, . . . , y_(M)) together havingfewer space-time dimensions than the space-time dimensions of thecombined antenna signals, and a plurality of signal processing unitseach having a plurality of inputs coupled to the common preprocessingunit for receiving all of the basis signals, each processing unitprocessing and combining said basis signals to produce a respective oneof a set of estimated received signals (z₀, . . . , z_(M)) each for acorresponding desired one of the users,

the common preprocessing section comprising

-   (i) means for maintaining through periodic updates a set of dominant    subspace filters, each of which being matched to one of the users of    interest, and the outputs of which being used by the subsequent    receiver sections, to be processed and combined in order to yield an    estimate of the desired signal for each user of interest;-   (ii) means for periodically estimating and/or updating the component    weights of the dominant subspace filters by correlation, with a    known training sequence or with the user's spreading code in a CDMA    system or with any other signal strongly correlated with the user of    interest's signal, in combination with appropriate temporal    averaging to isolate subspace-level information, as opposed to    instantaneous channel characteristics, and-   (iii) means for periodically or dynamically estimating and/or    updating the component weights (and/or any other parameters of    interest) of the receiver sections fed from the preprocessing    section in a manner and at a rate such that instantaneous channel    changes are tracked to provide a reliable and consistent estimate of    the desired signal.

Preferably, in embodiments of either aspect, when the array receiver isemployed in a code-division multiple access (CDMA) communicationssystem, the plurality of basis filters forming the common basis and theplurality of subsequent receiver signal processing units are bothmatched to:

-   (1) an ensemble of users sharing the same spreading code, if code    re-use is employed in the said communications system, or;-   (2) an ensemble of users with different codes, in which case the    array receiver system can further separate the users' signals and    possibly compensate discrimination problems due to code correlation,    power capture, etc.

In a CDMA system, the usual despreading can be performed at the outputsof the basis filters. Alternatively, the spreading operation at thetransmitter can be considered a part of the radio channel, in which caseit is natural to despread in the basis filters.

The array receiver of either aspect could also be employed at the basestation of a time-division multiple access (TDMA) communications systemor a frequency-division multiple access (FDMA) communications systemwhich does not employ carrier re-use. In such a case, the plurality ofdominant basis filters of the common preprocessing unit and theplurality of subsequent receiver signal processing units are bothmatched to an ensemble of users which are not perfectly separable intime and/or frequency, i.e. they interfere with one another either intime (e.g. because of dispersive channels) and/or in frequency (e.g.because of excess bandwidth due to imperfect channel filtering) thusleading to adjacent-channel interference (ACI).

Other aspects of the invention include an array receiver systemcomprising a receiver according to the first or second aspect, incombination with a said array antenna, and the corresponding method ofoperating the array receiver.

Thus, according to a third aspect of the invention, there is provided amethod of receiving signals from a plurality of transmitting users viaan array antenna comprising an array of N antenna elements providing aset of antenna signals (x₁, x₂, . . . , x_(N)), respectively, eachcomprising information from each user, the method comprising the stepsof receiving signals from a plurality of transmitting users via an arrayantenna having N antenna elements providing a set of antenna signals(x₁, x₂, . . . , x_(N)), respectively, each comprising information fromeach user, and being characterized by the steps of:

sampling each of the antenna signals;

preprocessing the samples of at least some of said antenna elementsignals (x₁, x₂, . . . , x_(N)) to form a plurality of basis signals(y₀, . . . , y_(M)) together having fewer space-time dimensions than thespace-time dimensions of the combined antenna signals, and

processing and combining said basis signals (y₀, . . . , y_(M)) toproduce a set of estimated received signals (z₀, . . . , z_(M)) each fora corresponding one of the users,

the preprocessing including the step of

-   -   combining all of the antenna signals (x₁, x₂, . . . , x_(N)) to        provide said plurality of basis signals (y₀, . . . , y_(M)) such        that each of the basis signals comprises a different combination        of the antenna signals,

the processing and combining step comprising the step of combining thebasis signals (y₀, . . . , y_(M)) to provide a series of user-specificoutput signals,

-   -   the method further comprising the step of periodically updating        parameters used for deriving each particular basis signal such        that each user-specific output signal will exhibit a desired        optimum concentration of energy of the received signal of that        particular user as received by the array antenna.

Preferred embodiments of this third aspect of the invention comprisemethod steps corresponding to the functions of embodiments of the arrayreceiver of the first and second aspect.

In one preferred embodiment, the receiver:

(1) maintains, through periodic updates, a set of dominant basisfilters, each of which is matched to one desired user among the users ofinterest, and the outputs of which are processed by the subsequentreceiver sections and combined in order to yield an estimate of thedesired signal for each desired user;

(2) periodically estimates and/or updates the component weights of thedominant subspace filters by correlation, with a known training sequenceor with the user's spreading code in a CDMA system or with any othersignal strongly correlated with the user of interest's signal, incombination with appropriate temporal averaging to isolatesubspace-level information, as opposed to instantaneous channelcharacteristics;

(3) periodically or dynamically estimates and/or updating the componentweights (and/or any other parameters of interest) of the receiversections fed from the prefiltering section in a manner and at a ratesuch that instantaneous channel changes are tracked to provide areliable and consistent estimate of the desired signal.

In embodiments of any of the first, second and third aspects of theinvention, the receiver may comprise a series of standard linear MMSEspace-time processors (i.e. one possible embodiment of the receiversections), one for each of the M+1 signals, operating on the transformedinput vector y[n]. However, this method only results in a net reductionof numerical effort if the number of signals M+1 taken into account issignificantly lower than the number of antenna elements N (in which casethe dimensionality of the input vector is reduced from N to M+1).

Embodiments of any of the three aspects of the invention may includespace-time matched filtering. This provides a much greater potentialcomplexity reduction and makes the invention more widely applicable.Thus, to further decrease computational cost, a logical extrapolation ofthe above concept is to extend the eigenfiltering to the temporal—aswell as the spatial—domain. In this case, only M+1 taps are left to beactively adapted (at every packet) for each user (as opposed to NL tapsfor a conventional system where N is the number of elements and L is therequired adaptive filter length. To achieve acceptable performance, itis normally required that M≦N; therefore this system will reduce thenumber of actively adapted taps by at least a factor of L.)

It so happens that a large portion of the ISI will in most cases behandled by the eigenfiltering, Indeed, the angle spread of an impingingsignal at the base station is typically narrow due to the height of thebase, i.e. most scattering activity then occurs in the immediatevicinity of the subscriber. This has the effect of making the covariancematrix of the signal under consideration heavily biased towards thefirst few eigenvalues [11]. A bank of primary eigenfilters eliminatesthe ISI associated with the first eigenvalue. Furthermore, it has beenshown that a memoryless combiner (such as those that follow theeigenfilter bank) has some ability to reduce ISI [13].

In cases where these two ISI reduction steps are not enough to warrantsatisfactory performance, more dimensions can be added to the dominantsubspace space-time filters to eliminate the ISI and CCI associated withthe secondary and subsequent eigenmodes at the cost of increasedcomplexity, since more taps will have to be actively adapted in thereceiver sections.

According to another embodiment of the invention, the receiverpreprocessing sections can include adaptive equalization, thus reducingthe need in certain cases for a large number of subspace dimensions toadequately handle the ISI.

The foregoing and other objects, features, aspects and advantages of thepresent invention will become more apparent from the following detaileddescription, taken in conjunction with the accompanying drawings, ofpreferred embodiments of the invention, which are described by way ofexample only.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified block schematic diagram of an array receiversystem having a receiver and an array antenna comprising an array ofantenna elements;

FIG. 2 is a more-detailed block schematic diagram of a part of thereceiver showing, in more detail, a dominant subspace filter for oneuser;

FIG. 3 is a flowchart depicting computation of updated weights for thedominant subspace filter;

FIG. 4 is a flowchart depicting computation of principal eigenvectorestimates for use in updating the weights;

FIG. 5 is a flowchart depicting computation of secondary eigenvectorestimates;

FIG. 6 is a block schematic diagram of a modified receiver system; and

FIG. 7 is a flowchart depicting adaptation of weights to changingchannel conditions.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

To facilitate understanding of the construction and operation of thepreferred ebodiments, some basic theory will first be presented.

As discussed, the optimal MMSE solution can be obtained through a linearcombination of all signals' matched-filters [7], [8]. Given an antennaarray and a dispersive (i.e. ISI-inducing) propagation environment, itfollows that the optimal MMSE solution can be obtained as a linearcombination of all signals' space-time matched-filters. Here, aspace-time filter matched to a given signal is a bank of N temporalfilters, each of which processes one of the N antenna elements' outputs,and whose outputs are combined to maximize the said signal's power withrespect to white noise and disregarding interference from the otherman-made signals.

This is advantageous in a multi-user SDMA context since the set ofmatched filters form a common basis which can be reused to obtain eachsignal's MMSE solution. In standard optimal architectures, independentcombiners (sets of weights) must be maintained for each user and theymust typically be recomputed from scratch at the start of a new packetbecause of the changing interference patterns. If a way can be found tomaintain with low computational cost a matched filter for every activeconnection, computing an MMSE solution for a given user and packetbecomes simply a matter of selecting the appropriate matched filters(corresponding to the active interferers in the packet of interest) andusing their outputs as inputs to standard MMSE processors (one perdesired user) which are adapted using the training sequence prefixes.The complexity of this approach is appealing when the system is designedin such a way that the number of inputs to the standard MMSE processorsis substantially reduced with respect to a system in which the antennaelements' outputs are directly processed.

One method to approximate the behaviour of a matched-filter withouthaving to track the multipath fading is to identify dominant subspacesof the users' vector channels. The said subspaces will contain most ofthe useful information yet vary at a much slower rate than the channelsthemselves. A dominant subspace is the reduced-rank space spanned by thefew most significant dimensions of the long-term eigenstructure of thechannel of interest. One case of interest (because it minimizescomplexity) consists in dominant subspaces with a single dimension. Whenthe corresponding eigenvector is used as a filter, the resulting deviceis termed an eigenfilter.

To obtain better estimates of the desired signals, it may be necessaryto perform eigenfiltering with subspaces having more than one dimension.The required number μ of dimensions for a given level of performance isa function of the propagation environment. Eigenfiltering overmultidimensional subspaces is termed hereafter dominant subspacefiltering.

Referring to FIG. 1, an array antenna receiver system for receivingsignals from a plurality of user transmitters (not shown) comprises anarray antenna having a plurality of antenna elements, specificallyNelements 22/1, . . . , 22/N, each coupled to a respective one of acorresponding plurality of RF front-end processing units 26/1, . . . ,26/N of an RF receiver section 26, which units treat the signals fromthe antenna elements to produce N signals x₁, . . . , x_(N),respectively. Each of the RF front-end units 26/1, . . . , 26/N has itsoutput coupled to the input of each of a set of M+1 filters specificallysubspace filters, 40/0, . . . , 40/M of a common preprocessing section40. Each of the subspace filters 40/0, . . . , 40/M is matched to arespective one of an ensemble of M+1 transmitting users, and has itsoutput coupled to the input of each of a corresponding plurality ofuser-specific signal processors 60/0, . . . , 60/M of a signalprocessing section 60. Each of the signal processors 60/0, . . . , 60/Mprocesses the respective set of the subspace signals y₀, . . . , y_(M)of the subspace filters 40/0, . . . , 40/M, respectively, to produce acorresponding one of a plurality of estimates z₀, . . . , z_(M) of thesignals s₀, . . . , s_(M) transmitted by the M+1 users.

The RE “front-end” units 26/1, . . . , 26/N are identical and ofconventional construction, so only one will be described, with referenceto the inset diagram of FIG. 1. As shown inset in FIG. 1, RF front-endunit 26/N comprises a low-noise amplifier (LNA) 28/N, a RF to IFdownconverter 30/N, a channel filter 32/N (which isolates the requiredchannel and rejects out-of-band noise and interference), and ananalog-to-digital converter 34/N for performing bandpass sampling.Alternatively, the IF or RF signal could be down converted to basebandprior to analog-digital conversion. The various alternatives andcompromises possible here are known to practitioners of the art. Theoutput of the A/D converter unit 34/N is also the output of the RFfront-end unit 26/N and is coupled to the input of each of the dominantsubspace filters 40/0, . . . , 40/M.

In each of the following embodiments, all of the dominant subspacefilters 40/0, . . . , 40/M are identical; although their structurediffers from one embodiment to another.

In the embodiment of FIG. 1, the dominant subspace filters 40/0, . . . ,40/M are principal space-time eigenfilters. Since they are allidentical, only the generic structure of the filter for a user m willnow be described, with reference to FIG. 2.

Although the performance analysis will be presented in the frequencydomain, the actual implementation can be made in the time domain. Theeigenfilters then take the form of banks of N tapped-delay lines 50/m₁,. . . 50/m_(N) each with a series of one-symbol delays, the number ofsuch delays being chosen to give a delay line length according to thetypical memory length of channels in the band of operation. In eachtapped delay line, a series of multipliers extract the delayed signalsfrom respective taps of the delay line and multiply each of them by arespective complex weight. For example, in delay line 50m₁, havingindividual delays 52m₁₁, . . . 52m_(1L), a series of multipliers 54m₁₁,. . . 54m_(1L) multiply the tapped signals by complex weights w₁₁, . . ., w_(1L), respectively, while, in delay line 50m_(N) having individualdelays 52m_(N1), . . . 52m_(NL), a series of multipliers 54m_(N1), . . .54 _(NL) multiply the tapped signals by complex weights w_(N1), . . .w_(NL), respectively. The other tapped delay lines are similar.

The outputs of the delay lines 50/m₁, . . . , 50/m_(N), i.e., thesignals from the multipliers 54m_(1L), . . . , 54 _(NL), respectively,are combined by a summer 52/m to form y_(m,1), the primary eigenfilteroutput for user m. It should be noted that there can be any number ofsuch eigenfilters whose combined outputs will make up the dominantsubspace filter output i.e. subspace signal y_(m). Thus, y_(m)=[y_(m,1). . . y_(m,μ)] where μ is the number of eigenfilters defining thedimensions of the dominant subspace. This estimate y_(m) is supplied toall of the signal processors 60/0, . . . , 60/M (FIG. 1).

The weights are dynamically adapted according to second-order statisticsas will now be described. A training sequence generator 42/m generates areplica of user m's known training sequence in synchronism withreception of said training sequence as part of a received packet. Theoutput s_(m)[k] of the training sequence generator 42/m is used by acovariance matrix estimator 44/m to estimate user m's current channelcovariance matrix R₁ across all space-time tap positions. This is thenused to update a running estimate of user m's long-term covariancematrix Σ_(m). An eigenvector estimator 46/m then uses the estimate ofΣ_(m) as a basis to estimate its principal eigenvector. The individualcomponents of said eigenvector constitute the current complex weightsw₁₁, . . . , w_(1L), . . . w_(NL) which are supplied to multipliers 54₁₁, 54 _(1L), . . . 54 _(NL) in filter bank 48/m for use in subsequentprocessing of the received signals.

The adaptation procedure used by the eigenfilters 50/m₁, . . . 50/m_(N)will now be described in detail with respect to the flowchart in FIG. 3,where the joint operation of a bank of 8 eigenfilters (corresponding to8 transmitting users) is detailed.

Adaptation of the eigenfilters requires that a running estimate of eachsignal's long-term covariance matrix be maintained. This estimate couldbe updated every time a packet containing known training symbols isreceived from the user of interest. Since the long-term statisticschange relatively slowly, however, the estimate update frequency for agiven user is going to be much lower than the frequency of occurrence ofthe training sequence (which typically is provided in every packet fromthe user of interest).

In step 3.1, the dominant subspace filter 40/m waits for the currentestimation interval to elapse (where “estimation interval” refers to therelatively long interval for long-term estimation as discussed above)and, in step 3.2, waits for the start of the next time slot. Assumingthat the known training sequence is a prefix and is thus at the start ofsaid time slot, in step 3.3 the portion of the received signalcorresponding to the training prefix is stored in a buffer for furtherprocessing.

Given that {hacek over (c)}_(m) is the “delay-extended” NL×1 vectorrepresenting the space-time signature of user m over all eigenfiltertaps, i.e.{hacek over (c)}_(m)=<c_(m) ^(H)[1], c_(m) ^(H)[2], . . . , c_(m)^(H)[L]>,  (6)where {hacek over (c)}_(m)[n]={hacek over (c)}_(m)(nT) is a sample ofthe vector impulse response of user m's channel at the array input takenat delay nT (nth multiple of the symbol period), in step 3.4, thedominant subspace filter 40/m obtains an estimated ĉ₀ of {hacek over(c)}₀ the vector impulse response for user 0 according to$\begin{matrix}{{{\hat{c}}_{0} = {\sum\limits_{k = 1}^{K}{{\overset{\sim}{x}\left\lbrack {{n + 1},k} \right\rbrack}{s_{0}\lbrack k\rbrack}}}},} & (7)\end{matrix}$where s₀[k] is user 0's training sequence (obtained from the trainingsequence generator 42/0 which is K symbols long and {hacek over(x)}[k,n] is the space-time received signal vector over all eigenfiltertaps corresponding to the kth training symbol of the nth trainingsequence. For example, for a fixed estimation interval of T_(c) secondsand a symbol period of T seconds,{hacek over (x)}[n,k]={hacek over (x)}(nT _(c) +kT).  (8)

In step 3.4, the eigenfilter covariance matrix estimator 44/m computesan estimate of the covariance matrix for user 0 for the current intervalaccording to $\begin{matrix}{{{\hat{R}}_{0}\lbrack n\rbrack} = {\frac{1}{K}{{\hat{c}}_{0}\lbrack n\rbrack}{{{\hat{c}}_{0}\lbrack n\rbrack}^{H}.}}} & (9)\end{matrix}$By definition, the long-term delay-extended covariance matrix for signal(s_(m)) is{hacek over (Σ)}_(m)=<{hacek over (c)}_(m){hacek over (c)}_(m)^(H)>.  (10)

Accordingly, a running estimate of {hacek over (Σ)}₀ (for user 0) isupdated in step 3.5. The estimator 44/m obtains a running estimate of{hacek over (Σ)}_(m) according to the following recursive relation:{circumflex over (Σ)}_(m) [n]=γ{circumflex over (Σ)} _(m)[n−1]+(γ−1){circumflex over (R)} _(m) [n],  (11)where {circumflex over (Σ)}_(m)[n] is the NL×NL long-term covariancematrix estimate after processing of the nth received training sequencefor the signal from user m, s_(m)[k] is the kth symbol in user m'straining sequence and γ is a forgetting factor chosen as a function oftraining update frequency and the rate of change of the long-termcovarance matrix in the propagation environment of interest. It islikely that γ would take on a value between 0.8 and 1 in most systems.

The covariance matrix estimator 44/m supplies the estimate {circumflexover (Σ)}₀ to eigenvector estimator 46/m which uses it in step 3.6 toestimate the principal eigenvector, thereby completing the weightestimation/update procedure for filter bank 48/0.

In this embodiment, the estimation of the principal eigenvector isperformed using the iterative power method [12]. This requires aninitial estimate of the eigenvector and an estimate of the covanancematrix (obtained in steps 3.5, 3.10, . . . , 3.14). Based on theseestimates, the dominant eigenvector of {circumflex over (Σ)}_(m) can beobtained according to:û_(m) ^((I))[n]={circumflex over (Σ)}_(m)[n]{hacek over (u)}_(m)^((I−1))[n],  (12)where û_(m) ^((I))[n] is the estimate in the nth estimation interval ofthe dominant eigenvector over all NL S-T eigenfilter taps after the ithiteration of the power method (including normalization, i.e. |û_(m)^((I))[n]|²=1). The convergence rate of the power method depends on theratio |λ₂|/|λ₁| where λ₁ and λ₂ are the largest and second largesteigenvalues of {hacek over (Σ)}_(m)[n]. For a well-conditioned matrixand any arbitrary starting vector {hacek over (u)}_(m) ⁽⁰⁾[n],convergence will normally occur within 10 iterations.

Upon network entry of a new user, a large number of iterations (50-100)night be necessary to guarantee a good estimate of the dominanteigenvector regardless of the eigenvalue distribution. Afterwards,however, since the successive covariance matrix estimates Σ_(m)[n] varylittle from one to the next, only a few (perhaps even 1 or 2) iterationsof the power method will be required between covariance matrix updates.

The eigenvector estimation procedure (performed by the eigenvectorestimators 46/0, . . . , 46/7) will now be described with reference tothe flowchart in FIG. 4 for user m.

In step 4.1, the estimator 46/m compares estimation interval index nwith 0; if n=0, then the first estimation is being performed since thegroup of users of interest has entered the network. Therefore, there isno previous estimate of the user m's principal eigenvector û_(m,0)[n]and an arbitrary estimate is used in step 4.2 to set the initialstarting point û_(m,0) ⁽⁰⁾[n]. In step 4.3, the number of iterations Iis set relatively high (50) since the starting point is not necessarilyclose to the real eigenvector.

If n>0 in step 4.1, then, in step 4.4, the estimator 46/m sets thestarting point û_(m,0) ⁽⁰⁾[n] to the eigenvector estimate û_(m,0)[n−1]obtained in the previous estimation interval. In step 4.5, it sets thenumber of iterations I to 5.

In step 4.6, the estimator sets the iteration index I to 0 and then, instep 4.7, performs a first iteration of the power method according toz^((I+1))={circumflex over (Σ)}_(m)[n]û_(m) ^((I))[n].  (13)

In step 4.8, the estimator normalizes vector z^((I+1)) to unity to yielda refined estimate û_(m) ^((I+1))[n]. In step 4.9, the estimatorverifies whether the prescribed number of iterations has been performed.If not, in step 4.10, it increments the iteration index I and repeatssteps 4.7 to 4.10 as indicated by the loop back to step 4.7. Thisprocess repeats itself until I=I−1. Finally, the final product of theiterative process û_(m) ^((I−1))[n] is assigned as the current principaleigenvector estimate û_(m)[n].

Every time a new update of the dominant eigenvector is obtained, itscomponents are inmediately transferred into the weight registers of theeigenfilter banks 50/m₁, . . . , 50/m_(N) (FIG. 2).

The same procedure is repeated in steps 5.8 through 5.14 for users 1 to7, i.e. filter banks 48/1 through 48/7.

In an alternative embodiment, corresponding to that shown in FIG. 3 butwith the addition of the items shown as dotted lines and boxes,secondary eigenvectors are also computed in steps 3.7, 3.11, . . . ,3.15, adding a second output to all dominant subspace filters 40/0, . .. , 40/M and thus providing more flexibility to the subsequent signalprocessors 60/0, . . . , 60/M. This can provide better performanceagainst intersymbol interference (ISI) and against co-channelinterference (CCI) and/or lessen the requirement for temporal processingin processors 60/0, . . . , 60/M, as explained below. In fact, anydesired number R of eigenvectors can thus be computed to achieve thedesired cost/performance compromise and/or the desired complexitybalance between the common preprocessing section 40 and the per-userprocessors 60/0, . . . , 60/M.

Estimation of secondary and further eigenvectors must be performed inorder of decreasing eigenvector importance. Afer estimating theprincipal eigenvector according to FIG. 4, the estimator subtracts itscontribution from the covariance matrix {circumflex over (Σ)}_(m)[n].The resulting covariance matrix, designated A₂, has a principaleigenvector which is approximately equal to the secondary eigenvector of{circumflex over (Σ)}_(m)[n]; the latter can therefore be estimated byfollowing the procedure described with reference to FIG. 4.

This procedure is detailed in FIG. 5 for an arbitrary number R ofdominant eigenvectors. In step 5.1, the eigenvector order index r is setto 1, indicating the principal eigenvector. In step 5.2, the initialcovariance matrix A₁ is set to {circumflex over (Σ)}_(m)[n]. An estimateof the principal eigenvector u_(m)[n] is then obtained in step 5.3 basedon A_(r) and according to the procedure outlined in FIG. 4. Theeigenvector estimator 46/m then verifies in step 5.4 whether all Reigenvectors have been computed. If not, in step 5.5, it computes anestimate of the rth order eigenvalue according to{circumflex over (λ)}_(r)=û_(m,r) ^(H)A_(r)û_(m,r).  (14)and, in step 5.6, subtracts the corresponding eigenvector from A_(r)according toA _(r+1) =A _(r)−{circumflex over (λ)}_(r) û _(m,r) û _(m,r) ^(H).  (15)It then increments the index r in step 5.7 and repeats the steps 5.3 to5.6 for the subsequent eigenvectors.

The output signals y₁, . . . , y_(M) from the dominant subspace filters40/0, . . . , 40/M, i.e., the outputs of the common preprocessingsection 40, are used by per-user signal processors 60/0, . . . , 60/M toprovide user-specific estimated received signals z₀, . . . , z_(M),respectively, which are estimates of the M+1 desired signals, eachsignal processor using the outputs of all of the dominant subspacefilters 40/0, . . . , 40/M to produce its respective estimate.

It will be appreciated that the comnmon preprocessing section 40described with reference to FIGS. 2 to 5 yields a set of signals with areduced number of dimensions for further processing. This set ofsignals, or basis, is adapted through long-term adaptation since ittracks only the subspace structure of the channels, not theirinstantaneous behaviour.

This long-term loop (which corresponds to FIG. 3) need only be performedonce every tenth of a second. This estimation interval can correspond toseveral hundred packets. On the other hand, the method associated withthe per-user processors 60/0, . . . 60/M is a short-term loop (to bedescribed hereafter for the preferred embodiment with reference to FIG.7) which typically must be performed once per packet.

The signal processors 60/0, . . . 60/M can take a number of forms.According to this preferred embodiment where the dominant subspacefilters 40/0, . . . , 40/M are space-time principal eigenfilters, asdescribed previously, the signal processors 60/0, . . . , 60/M simplyomprise weight-and-sum structures across the eigenfilter outputs, asillustrated in FIG. 6.

Referring to FIG. 6, signal processors 60/0, . . . , 60/M are identicalso only one will be described, namely signal processor 60/0. Itcomprises a plurality of multipliers 64/0 ₀₀, . . . , 64/0 _(M) whichapply weights w₀₀, . . . , w_(0M) to subspace signals y₀, . . . , y_(M),respectively, from the dominant subspace filters 40/0, . . . , 40/M. Aswill be described later, the weights w₀₀, . . . , w_(0M) are derived independence upon substantially instantaneous channel characteristics, andupdated. Means for deriving and updating these weights would be known topersons skilled in this art and so, for purposes of clarity, this meansis not shown in FIG. 6

The weighted signals are then summed by combiner 62/0 whose output z₀ isfed to the detector 80/0 (see also FIG. 1). It should be noted that eachof the other signal processors 40/1, . . . , 40/M also uses all of theoutput signals y₀, . . . , y_(M) to obtain its respective one of outputsz₁, . . . , z_(M).

It is also important to note that, should transmission be momentarilyinterrupted (such as in bursty data communications), no problem occurssince the weights are estimated afresh in every interval.

In step 7.1, the eigenvector estimator 46/m (FIG. 2) waits for the nextpacket to start. Upon acquiring a packet, in step 7.2 the estimatorstores the portion of the packet which corresponds to the trainingsequence in a buffer (not shown). In step 7.3, it computes the estimateof the (M+1)×(M+1) short-term covariance matrix R_(yy) of theeigenfilter bank's output vector y as follows: $\begin{matrix}{{\hat{R}}_{yy} = {\frac{1}{K}{\sum\limits_{k = 1}^{K}{{y\lbrack k\rbrack}{{y\lbrack k\rbrack}^{H}.}}}}} & (16)\end{matrix}$No training sequence is necessary since, at this point, there is no needto discriminate between individual transmitted signals. Furthermore, thesame matrix R_(yy) is used by all users so this step need only beperformed once per loop if all users' training sequences aresynchronized.

In step 7.4, the estimator sets user index m to 0 and in step 7.5 itestimates user m's (M+1)×1 signature vector over the eigenfllter bank'soutputs using the known training sequence transmitted by user m, i.e.$\begin{matrix}{d_{m} = {\frac{1}{K}{\sum\limits_{{Lk} = 1}^{K}{{y\lbrack k\rbrack}{{s_{m}^{*}\lbrack k\rbrack}.}}}}} & (17)\end{matrix}$

The training sequence will be provided by the training sequencegenerator 42/m which is part of eigenfilter 40/m (FIG. 2). The generator42/m will then occasionally (when there is an intersection with thelonger estimation interval) feed the “long-term” estimation in dominantsubspace filter 40/m and the “short-term” estimation in processor 60/msimultaneously.

Indeed, although the “short-term” loop of FIG. 7 typically will beperformed on the order of 100 times more often than the “long-term” loopof FIG. 3, both can use the same training sequences, presumablyavailable in every packet. However, the long-term loop will obviouslyuse fewer of them.

It should be noted that the “short-term” covariance matrix and thechannel estimates all should preferably be estimated over the same setof received samples (which correspond to training sequences sentsimultaneously by all users) to ensure statistical consistency andprevent serious performance degradation.

In step 7.6, the estimator computes the weight vector w_(m) whichminimizes the mean-square error according tow_(m)={circumflex over (R)}_(yy) ⁻¹{circumflex over (d)}_(m).  (18)

In step 7.7 the estimator 46/m transfers the weights obtained thereby tothe signal processor 60/m. The estimator 46/m then determines, in step7.8, whether the weights in all signal processors have been updated. Ifthey have, the estimator returns to step 7.1 to await the next time slotand repeat the procedure. If they have not, the estimator incrementsindex m in step 7.9 and repeats steps 7.5 through 7.8 to update the nextprocessor. Steps 7.5 to 7.9 are repeated until all of the processors60/0, . . . , 60/M have had their weights updated and step 7.8 findsm=M.

The embodiment described above with reference to FIG. 6 is designated“space-time eigenfiltering followed by MMSE combining”.

To recapitulate, embodiments of the present invention can be describedin algorithmic fashion as comprising two loops, vis long-term andshort-term. The long-term loop can be summarized as follows:

For every user m,

-   (i) The short-term covariance matrix of user m's signature over all    NL taps of eigenfilter m is estimated on the basis of a known    training sequence transmitted by user m;-   (ii) The short-term estimate is used to update a running estimate of    the long-term-averaged covariance matrix of user m's channel    estimate (eqn. 11);-   (iii) Using the running estimate of the long-term covariance matrix    and the estimate of its dominant eigenvector from the previous    iteration as a starting point, said running estimate is updated by    performing one or more iterations of the power method (eqn. 12). If    secondary eigenfilters are implemented, the same procedure applies    for updating the secondary eigenfilter except that the dominant    eigenvector is a priori subtracted from the covariance matrix    estimate.-   (iv) The computed weights (i.e. elements of the estimated    eigenvector(s)) are transferred to the mth dominant subspace filter.-   (v) The start of the next long-term training interval is awaited,    and steps (i) to (iv) then are repeated.

The short-term loop can be summarized as follows:

-   -   1. The (M+1)×(M+1) short-term covariance matrix R_(yy) of the        eigenfilter bank's outputs is estimated;        For every user m,    -   2. User m's (M+1)×1 signature vector is estimated over the        eigenfilter bank's outputs;    -   3. The weight vector w_(m)={circumflex over (R)}_(yy)        ⁻¹{circumflex over (d)}_(m) is computed;    -   4. The weights are transferred to the mth combiner.    -   5. The start of the next short-term training interval (next        packet transmission by mth user) is awaited, and then the loop        (steps 2 to 4) is repeated.

Alternative Embodiments

The signal processors 60/0, . . . , 60/M could also includeequalization, thus performing space-time processing. Such an extensionis relatively straightforward to one skilled in the art and has theadvantage of improving the performance (at the cost of additionalcomplexity) in terms of signal quality and/or alleviating the need formany subspace dimensions in the preprocessing section 40 in order toobtain a given level of performance.

In an alternative embodiment, the eigenfilter banks 48/0, . . . , 48/Mperform strictly spatial processing, leaving all temporal processing tothe per-user signal processors 60/0, . . . , 60/M. The eigenfilter banks48/0, . . . , 48/M then have the structure depicted in FIG. 2 but withonly one column of weights, i.e. L=1. Such an embodiment is designated“spatial eigenfiltering followed by MMSE space-time processing”.

It should be noted that the latter embodiment provides betterperformance gains but a smaller complexity reduction than “space-timeeigenfiltering followed by MMSE combining”. In fact, a net complexityreduction compared with respect to conventional space-time processingcan only be obtained if the number of antenna elements is greater thanthe number of users. However, this is usually the case even inconventional space-time processors in order to provide some gain againstmultipath fading in addition to spatial discrimination of users'signals.

While it is general practice to assume that the channels can beconsidered static over the length of a block (i.e., the length of ablock is significantly smaller than the channel correlation time), thepresent invention is applicable equally well in other cases wherecontinuous tracking (using adaptive algorithms such as theleast-mean-square (LMS) or the Kalman filtering algorithm) is necessary.

If, in fact, continuous tracking is implemented, it may not be necessaryto provide frequent training sequences. Indeed, both subspace filteringand weight computation updates can be performed using past decisions astraining symbols, provided the latter are reliable (“decision-directedadaptation”). Training sequences, while less frequent, would still berequired to: (1) initialize the system when a new link was formed sothat its first decisions would be reliable enough to start the trackingprocedure; and (2) periodically reset the system to minimize errors dueto tracking.

Blind adaptation techniques could also be used, in which case trainingsequences would not be required at all. Likewise, the principles of theinvention apply equally well to analog waveforms as opposed todigitally-modulated signals.

The transmitting stations need not be limited to using a single antenna.If they have multiple antennas, thus forming multiple-input,multiple-output (MIMO) links, embodiments of the invention as describedhere can be modified appropriately in a number of ways while retainingthe essence and advantages of the invention. For example, eachtransmitter antenna element belonging to the same user could have at thereceiver its own donminant subspace filter. Thus, an ensemble ofdominant subspace filters would feed a single per-user signal processorwhich could perform standard MIMO reception techniques such as layeredspace-time (LST) successive cancellation.

Error-correction coding, whether unidimensional or bidimensional (inMIMO links), can also be incorporated in ways that should be obvious toa skilled practitioner of the art.

Likewise, a variety of alternatives to linear MMSE processing can beconsidered for the per-user processors 60/0, . . . , 60/M withoutdeparting from the scope of the invention. Possibilities includedecision-feedback processing, delayed decision-feedback, multi-user orMIMO decision-feedback maximum-likelihood sequence estimation (MLSE),etc.

The basis signals matched to each user could be formed using alternativetechniques which are not based on the subspace structure of thechannels. They could, for example, be based on estimates of the maindirections-of-arrival characterizing each user's signal.

The invention can also be applied to CDMA systems. Thus, for example,the usual despreading could be performed at the outputs of the subspacefilters 40/0 . . . 40/M. Alternatively, a bank of despreaders could beprovided at the input of the preprocessing section 40 and supply all ofthe despread signals to each of the dominant subspace filters,

Complexity Reduction

For the purpose of comparing complexity, an example will be consideredof a 10 Mb/s system with packets of 68 bytes (roughly the size of an ATMcell). A guard byte is inserted between each pair of successive packets.If there are 8 users (i.e. M=7) who send packets simultaneously onceevery ten slots on the same carrier, since there are 18115.94 slots persecond, the users of interest are transmitting at a rate of 1811.59packets per second. At this rate, channels typically will besufficiently different from one packet to the next due to multipathfading to warrant retraining of the per-user processors 60/0 . . . 60/Mat every packet. It will also be assumed that all adaptive filters havea length of 10 symbol-spaced taps; each packet contains a known trainingsequence of 32 bits; and the array has 10 elements (i.e. N=10)

The long-term covariance matrix is assumed to have a worst-case 90%correlation time of 0.5 s [9]; its estimate will be estimated every 0.1s and the power iteration will also be performed every 0.1 s.Furthermore, it is assumed that diagonal loading (i.e. adding a smallconstant to all elements of the diagonal) is used whenever it isnecessary to invert a covariance matrix larger than the length of thetraining sequence (i.e. larger than 32×32) since it might otherwise besingular under these conditions.

Table 1 illustrates the relative numerical complexity of the twoproposed structures versus standard MMSE space-time processing in termsof the number of multiplication and addition operations required. Allfigures are totals for all 8 users. TABLE 1 Relative numericalcomplexity of proposed structures compared with conventional MMSEspace-time processing. spatial S-T standard eigenfilter + eigenfilter +MMSE S-T MMSE MMSE comb. long-term 8267200 mult. 15320 mult. 1413200mult. adaptation 8260550 adds. 14800 adds. 1372000 adds. (per iteration)short-term 1.498 · 10¹⁰ mult. 4271360 mult. 7808 mult. adaptation 1.496· 10¹⁰ adds. 4266840 adds. 7644 adds. (per iteration) TOTAL 7.738 · 10⁹mult. 2.828 · 10⁷ mult. (per second) 7.730 · 10⁹ adds. 2.757 · 10⁷ adds.Theory of Operation

While not wishing to be limited by theory, an explanation of the theoryof operation will now be given to facilitate understanding of thepreferred embodiments.

Given an eigendecomposition of the long-term average covariance matrixof the signal (i.e. its subspace structure), the eigenvectorcorresponding to the largest eigenvalue constitutes what will be calledhere the primary eigenfilter, Formally, the N×N long-term correlationmatrix (where N is the number of receiving antenna elements at the basestation) of signal s_(m)(t) (transmitted by the mth out of M+1 users) ina flat-fading environment can be defined asΣ_(m)=<x_(m)(t)x_(m)(t)^(H)>,  (19)wherex_(m)(t)=c_(m)(t)s_(m)(t),  (20)and x_(m)(t) is the received signal from user m, c_(m)(t) is the N×1baseband equivalent vector channel between user m and the array (alsocalled user m's spatial signature), s_(m)(t) is the useful transmittedsignal from user nm and the expectation <•> can be interpreted as eitherthe time average over a period of time long enough to eliminateshort-term channel fluctuations or an ensemble average over thedistribution of possible channel realizations.

It is well-known that, in almost all terrestrial propagationenvironments narrowband (i.e. flat fading) wireless channels can beaccurately represented in the short-term as either zero mean(Rayleigh-type fading) or non-zero mean (Rician-type fading) complexgaussian variables. It follows that the vector c_(m) taken at any timeinstant is a complex gaussian vector characterized by its long-termcovariance matrix (which is equal to Σ_(m) in the Rayleigh-fading case)and its mean vector μ_(m) whereΦ_(m)=<(c _(m)−μ_(m)) (c _(m)−μ_(m))^(H)>,  (21)is the general definition of the long-term covariance matrix of user m'svector channel.

Without loss of generality, the remainder of this description willassume that the fading is Rayleigh and user m's covariance matrix canthus be denoted by Σ_(m) without ambiguity.

For frequency-selective fading channels, the correlation matrix can bedefined as a frequency-dependent matrix:Σ_(m)(f)=<ℑ[c_(m)(t,τ),τ]ℑ[c_(m)(t,96 ),τ]^(H)>_(t),  (22)where ℑ[•,τ] denotes the Fourier transform taken over the delay τvariable and <•>_(t) denotes averaging over the time variable t. Also,the dispersive channel impulse response c_(m)(t,τ) is defined as theecho received at time t+τ originating from an impulse sent at time t.

Consideration will be given first to the primary spatial eigenfilter inthe general case of frequency-selective fading channels. For spatialfiltering only, development proceeds from a “frequency-flat” covariancematrix for signal m, obtained by further frequency- or delay-averaging:$\begin{matrix}{{{\overset{\_}{\Sigma}}_{m}{\frac{1}{f_{\max} - f_{\min}}{\int_{f_{\min}}^{f_{\max}}{{\Sigma_{m}(f)}{\mathbb{d}f}}}}} = {< {{c_{m}\left( {t,\tau} \right)}{c_{m}\left( {t,\tau} \right)}^{H}} >_{{({t,\tau})}^{\prime}}}} & (23)\end{matrix}$

It should be noted that although the analysis presented here is in thefrequency domain, the preferred embodiments described hereinafter areimplemented in the time domain.

The two definitions for Σ_(m) (in the delay domain and the frequencydomain) can be proven equivalent through Parseval's relation.

The primary spatial eigenfilter for signal m is then simply theeigenvector of Σ _(m) corresponding to its largest eigenvalue. Itslength is equal to the number of antenna elements and it isimplementable as a set of weights used in combining the outputs of thearray.

Statistically, it can be shown to be the fixed combination of weightsproviding the highest average signal output without tracking themultipath fading. Some analytical and simulation results have indicatedthat the long-term correlation matrix changes relatively slowly evenwith mobile subscribers and can in general be assumed fixed for periodsof the order of a second [9]. This assumption has also been exploited toform the basis of downlink beamforming systems in [10] and [11]. In thebroadband wireless context, it is reasonable to expect that the rate ofchange would be even slower since the subscribers are fixed. Thisimplies that, in all cases, the eigenfilters can be computed in thebackground using a long-term tracking adaptation system demanding anegligible numerical effort. This is where the complexity advantage ofthis invention lies, as described in the “preferred embodiments”section.

If there are M+1 signals and the desired signal is signal s₀, let usdefine a spatial transformation on the input vector which can take theformy[n]=Ux[n]=Uδ[n]*x[n],  (24)where δ[n] is the Kronecker delta function, x[n] is the array inputvector at time index n and $\begin{matrix}{{U = \begin{bmatrix}u_{0}^{H} \\u_{1}^{H} \\\vdots \\u_{M}^{H}\end{bmatrix}},} & (25)\end{matrix}$is an (M+1)×N matrix with u_(m) being the primary spatial eigenfilter ofthe mth signal as described above. At the output of this transformation,the correlation matrix of the mth signal is expressed{tilde over (Σ)}_(m)(f)=UΣ_(m) (f)U^(H) for all fε[f_(min),f_(max)],  (26)and the desired signal signature at the output of transformation Ubecomesd₀(f)=Uc₀(f) for all fε[f_(min), f_(max)],   (27)where c₀(f) is the desired signal signature prior to the transformation.

It follows that the performance of an MMSE space-time combiner using thevector y[n] as input can be analyzed using known techniques and formulasfor standard linear MMSE adaptation parametrized on the modifiedcovariance matrices.

The rest ofthis section comprises a performance analysis in thefrequency domain of the embodiment with space-time subspace filtering,without adaptive equalization and further assuming without loss ofgenerality that the dominant subspace filters have one dimension.

Development proceeds by defining the prefiltering transformation (i.e.the eigenfilter bank) corresponding to the primary eigenvectors on“frequency-extended” space-time vectors: $\begin{matrix}{{\overset{\Cup}{y} = {{\overset{\Cup}{y}}_{1} = {{\overset{\Cup}{U}}_{1}\overset{\Cup}{x}}}},{where}} & (28) \\{{\overset{\Cup}{x} = {\Delta_{b}\left\lbrack {{x^{H}\left( {f_{\min} + \frac{\Delta_{b}}{2}} \right)},{x^{H}\left( {f_{\min} + \frac{3\Delta_{b}}{2}} \right)},\ldots\quad,{x^{H}\left( {f_{\max} - \frac{\Delta_{b}}{2}} \right)}} \right\rbrack}},} & \left( 29 \right.\end{matrix}$is the NN_(b)×1 frequency-extended array input vector obtained bysplitting the band of interest into N_(b) bins of width Δ_(b)significantly smaller than the coherence bandwidth (i.e. the fading canbe considered flat within a single bin). Likewise {hacek over (y)} isthe (M+1)N_(b)×1 frequency-extended vector of the eigenfilter outputsand is expressed $\begin{matrix}{{{\overset{\Cup}{y}}^{H} = {\Delta_{b}\left\lbrack {{y^{H}\left( {f_{\min} + \frac{\Delta_{b}}{2}} \right)},{y^{H}\left( {f_{\min} + \frac{3\Delta_{b}}{2}} \right)},\ldots\quad,{y^{H}\left( {f_{\max} - \frac{\Delta_{b}}{2}} \right)}} \right\rbrack}},} & (30)\end{matrix}$where each element of vector {hacek over (y)} is a complex numberrepresenting a superposition of flat fading channels and y(f) is theM+1×1 frequency-dependent output vector of the space-time eigenfilterbank. Also, it should be noted that {hacek over (y)} is vector of size(M+1)N_(b)×1. Also, $\begin{matrix}{{{\overset{\Cup}{U}}_{1} = \begin{bmatrix}{\overset{\Cup}{u}}_{10}^{H} \\{\overset{\Cup}{u}}_{11}^{H} \\\vdots \\{\overset{\Cup}{u}}_{1M}^{H}\end{bmatrix}},} & (31)\end{matrix}$where {hacek over (u)}_(1m) is the frequency-extended form of theprincipal space-time eigenfilter corresponding to signal s_(m).

All those quantities are time-varying (except for U which is fixed inthe short-term) but time dependence is omitted for clarity.

The system performance can be assessed by defining “frequency-crunched”channel vectors: $\begin{matrix}{{\overset{\_}{d_{m}} = {\sum\limits_{k = 1}^{N_{b}}{{d_{m}\left( {f_{\min} + \frac{\left( {{2k} - 1} \right)\Delta_{b}}{2}} \right)}\Delta_{b}}}},} & (32)\end{matrix}$where $\Delta_{b} = \frac{f_{\max} - f_{\min}}{N_{b}}$is the width of each aforementioned frequency bin and d_(m)(f) is an(M+1)×1 vector defining user m's signature after the transformation{hacek over (U)} such that its frequency extended versiond {hacek over(d)}_(m)={hacek over (U)}{hacek over (c)}_(m). Furthermore, {hacek over(c)}_(m) is a frequency-extended vector defined in the same manner as ineqn. (29).

The above operation can be rewritten as a filter linear transformationof the formd _(m)=V{hacek over (d)}_(m),  (33)where V is an (M+1)×(M+1)N_(b) matrix of the form $\begin{matrix}{{V = \begin{bmatrix}1 & 0 & 0 & \left( {M - {1\quad{zeros}}} \right) & \cdots & 1 & 0 & 0 & \left( {M - {1\quad{zeros}}} \right) \\0 & 1 & 0 & \left( {M - {1\quad{zeros}}} \right) & \cdots & 0 & 1 & 0 & \left( {M - {1\quad{zeros}}} \right) \\0 & 0 & 1 & \left( {M - {1\quad{zeros}}} \right) & \cdots & 0 & 0 & 1 & \left( {M - {1\quad{zeros}}} \right) \\\vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots\end{bmatrix}},} & (34)\end{matrix}$and {hacek over (d)}_(m) is the frequency-extended version of thesignature d_(m). The corresponding short-term covariance matrix is givenby $\begin{matrix}{{\overset{\_}{R}}_{I + N} = {{\sum\limits_{m = 1}^{M}{{\overset{\_}{d}}_{m}{\overset{\_}{d}}_{m}^{H}}} + {I_{M + 1}\sigma^{2}}}} & (35)\end{matrix}$

The output of a linear MMSE combiner taking y as input but which doesnot perform temporal processing (i.e. equalization) can be writtenz={hacek over (w)}^(H){hacek over (y)}.  (36)

Since only a single weight multiplies the output of each space-timefilter, the extended weight vector is spectrally flat. In other words,w(f)=w.   (37)

Therefore the extended weight (M+1)N_(n)×1 vector has the form{hacek over (w)}^(H)=[w^(H), w^(H), . . . , w^(H)].  (38)

This implies that the output can be expressed $\begin{matrix}{{z = {w^{H}\overset{\_}{y}}},{where}} & (39) \\{\overset{\_}{y} = {\Delta_{b}{\sum\limits_{k = 1}^{N_{b}}{{y\left( {f_{\min} + {\left( {{2k} - 1} \right)\frac{\Delta_{b}}{2}}} \right)}.}}}} & (40)\end{matrix}$

This can also be written as a linear transformationy=V{hacek over (y)}.  (41)

From MMSE filtering theory, the optimal weight vector in this context isw_(opt)= R _(I+N) ⁻¹ d ₀.  (42)Therefore, the optimum MMSE performance of space-time eigenfilteringfollowed by a linear MMSE combiner isμ₀= d ₀ ^(H) R _(I+N) ⁻¹ d ₀.  (43)

By virtue of the transformation in equation (33), it is obvious that d ₀is a complex gaussian vector with covariance matrixΣ ₀=V{hacek over (U)}Σ₀{hacek over (U)}^(H)V^(H),  (44)and that R _(I+N) is equivalent to a short-term interference-plus-noisecovariance matrix obtained from a fictional M+1 element array in a flatfading environment. Each of its interference terms is obtained from acomplex gaussian vector parametrized on appropriately transformedcovariance matrices as per equation (44).

While this approach reduces complexity considerably compared to standardspace-time processing by exploiting a multiuser channel basis, it doesso at the cost of reduced performance. Specifically, the robustnessagainst fading (ISI) will be reduced somewhat. In cases where this isnot acceptable, the system can be augmented by the addition of a seconddimension to the subspaces (thus implementating secondary eigenfilters)or more dimensions to the subspace filters.

The output of the secondary bank of eigenfilters is obtained, as inequation (28), by defining a prefiltering transformation correspondingto the secondary eigenvectors: $\begin{matrix}{{{\overset{\Cup}{y}}_{2} = {{\overset{\Cup}{U}}_{2}\overset{\Cup}{x}}},{where}} & (45) \\{{{\overset{\Cup}{U}}_{2} = \begin{bmatrix}{\overset{\Cup}{u}}_{20}^{H} \\{\overset{\Cup}{u}}_{21}^{H} \\\vdots \\{\overset{\Cup}{u}}_{2M}^{H}\end{bmatrix}},} & (46)\end{matrix}$and {hacek over (u)}_(2m) is the principal space-time eigenfiltercorresponding to signal (s_(m)).

It follows that in a system extended to include a secondary eigenfilterbank, the overall output vector of the prefiltering section is defined:$\begin{matrix}{\overset{\Cup}{y} = {\begin{bmatrix}y_{10} \\\vdots \\y_{1M} \\y_{20} \\\vdots \\y_{2M}\end{bmatrix} = {\begin{bmatrix}{\overset{\Cup}{y}}_{1} \\{\overset{\Cup}{y}}_{2}\end{bmatrix} = {\begin{bmatrix}{\overset{\Cup}{U}}_{1} \\{\overset{\Cup}{U}}_{2}\end{bmatrix}{\overset{\sim}{x}.}}}}} & (47)\end{matrix}$In this context, user m's signature becomes $\begin{matrix}{{{\overset{\Cup}{d}}_{m} = {\begin{bmatrix}{\overset{\Cup}{U}}_{1} \\{\overset{\Cup}{U}}_{2}\end{bmatrix}{\overset{\Cup}{c}}_{m}}},} & (48)\end{matrix}$after the prefiltering transformation. Likewise, user m'sfrequency-crunched signature becomes: $\begin{matrix}{{{\overset{\_}{d}}_{m} = {\begin{bmatrix}V \\V\end{bmatrix}{\overset{\Cup}{d}}_{m}}},} & (49)\end{matrix}$

The rest of the development is identical to the case where only theprimary eigenfilters are used. Similar generalizations can likewise bedevised in a straightforward fashion for dominant subspaces with anynumber of dimensions.

It should be noted that like the MMSE combiners, the eigenfilter basisitself is also implemented using a series of taps in a space-timearrangement. However, two factors conspire to make the amount of workinvolved in adapting these taps negligible.

-   -   Since the eigenfilters can be considered fixed in the        short-term, the adaptation takes place in a very long-term        context compared with the MMSE combiner taps. The proposed        adaptation scheme here is based on the power iteration method.    -   Only one basis is required to accommodate a plurality (up to        M+1) of MMSE combiners.

INDUSTRIAL APPLICABILITY

Embodiments of the invention would be useful in receivers in stationsthat receive multiple signals simultaneously, such as (i) base stationsin cellular communications systems or access points in wireless LANs;(ii) relay stations or terminal stations in ad hoc or unlicensed orpacket radio networks capable of maintaining multiple linkssimultaneously; and (iii) terminal or access points of multiple-inputmultiple-output (MIMO) systems.

Embodiments of the present invention may provide a less costly solutionin terms of the processing power, the hardware complexity, or both. Infact, they can provide a reduction in complexity of an order ofmagnitude with respect to a canonical linear space-time receiver, yetwith minimal performance degradation.

It should be appreciated that the present invention is not limited tothe foregoing embodiments but could be applied equally well in othercases where continuous tracking (using adaptive algorithms such as LMS)is necessary.

The reduced complexity aspect of preferred embodiments of the presentinvention stems from (i) the shared nature of the common preprocessingsection, i.e. it is reused for all users; and (ii) the fact that it isadapted slowly, i.e. is less demanding in terms of hardware and/orsoftware complexity.

It will be appreciated that the invention is not limited to receiversemploying space-time processing but embraces receivers employingspace-frequency processing, for example using Fast Fourier Transforms,or even strictly spatial processing.

Although embodiments of the invention have been described andillustrated in detail, it is to be clearly understood that the same areby way of illustration and example only and not to be taken by way ofthe limitation, the scope of the present invention being limited only bythe appended claims.

Definitions

In this Specification:

A “channel” refers to the relationship between a transmitted signal anda corresponding received signal.

A “vector channel” refers either to a channel with a single input andmultiple outputs (SIMO) or a channel with multiple inputs and a singleoutput (MISO). Each entry in a channel vector describes the amplitudeand phase of the corresponding channel component.

A “dispersive channel” or “wideband channel” is a channel with animpulse response significantly longer than a symbol period, thusresulting in overlap between subsequent transmitted symbols, i.e.intersymbol interference (ISI). Such a channel cannot be describedadequately with a single complex gain. It can either be described as acontinuous or discrete (i.e. symbol-spaced samples) function of delay inthe time domain, or as a continuous function of frequency in thespectral domain.

A “narrowband channel” or “flat fading channel” has an impulse responseshorter than a symbol period and can thus be described by a singlecomplex gain (or a vector of the same in the case of a vector channel).

A “space-time channel” is a vector channel which is also dispersive andcan thus be described as a space-time matrix of complex gains, or afrequency-dependent vector.

A “covariance matrix” is a time or frequency-averaged outer product of avector quantity or a matrix quantity. In the context of this disclosure,either quantity is either a vector signal or a vector channel. Thecovariance matrix of a wireless channel characterizes the long-termstatistics which underlie the rapid and random instantaneousfluctuations typical of such channels. The fact that such covariancematrices vary at a much slower rate than the channels they characterizeis exploited by embodiments of the present invention to reduce theircomplexity.

A channel “subspace” is a multidimensional space made up of a subset ofthe dimensions making up the N-dimensional space characterizing anN-element channel vector. A “dominant subspace” is a subspacecorresponding to the most significant dimensions of the channel, i.e, asubset of orthogonal directions which, on average, contain most of thechannel's energy.

REFERENCES

-   [1] J. G. Proakis, Digital Communications, 3rd ed. New York:    McGraw-Hill, 1995, pages 152-163.-   [2] S. Verdu, Multiuser Detection. Cambridge: Cambridge University    Press, 1998, pages 154-213.-   [3] R. D. Gitlin et al., U.S. Pat. No. 6,188,718, “Methods and    apparatus for reducing cochannel interference in a mixed-rate    communication system,” issued Feb. 13, 2001.-   [4] C. H. Barratt, U.S. Pat. No. 5,592,490, “Spectrally efficient    high capacity wireless communication systems,” issued Jan. 7, 1997.-   [5] R. H. Roy, III and B. Ottersten, U.S. Pat. No. 5,515,378,    “Spatial division multiple access wireless communication systems,”    issued May 7, 1996.-   [6] B. Ottersten et al., U.S. Pat. No. 5,828,658, “Spectrally    efficient high capacity wireless communuication systems with    spatio-temporal processing,” issued Oct. 27, 1998.-   [7] J. Salz, “Digital transmission over cross-coupled linear    channels,” AT&T Tech. J., vol. 64, no. 6, July-August 1985, pp.    1147-1159.-   [8] B. R. Petersen and D. D. Falconer, “Equalization in    cyclostationary interference,” Technical Report SCE-90-01, Dept. of    Systems and Computer Engineering, Carleton University, January 1990.-   [9] S. Roy and D. D. Falconer, “Modelling the narrowband base    station correlated diversity channel,” in Proc. CTMC'99, Vancouver,    Canada, June 1999.-   [10] C. Farsakh and J. A. Nossek, “Spatial covariance based downlink    beamforming in an SDMA mobile radio system,” IEEE Trans. Comm., vol.    46, no. 11, pp. 1497-1506, November 1998.-   [11] D. Gerlach, Adaptive Transmitting Antenna Arrays at the Base    Station in Mobile Radio Networks, PhD dissertation, Stanford    University, Stanford, U.S., August 1995.-   [12] G. H. Golub and C. F. Van Loan, Matrix Computations. Baltimore;    Johns Hopkins University Press, 1989.-   [13] M. V. Clark, Diversity and Equalization in Digital Cellular    Radio, PhD disseration, University of Canterbury, Christchurch, New    Zealand, 1992.

1. An array receiver for processing signals received from a plurality oftransmitting users via an array antenna having an array of N antennaelements providing a set of antenna signals (x₁, x₂, . . . , x_(N)),respectively, each comprising information from each user, wherein saidreceiver has a common preprocessing section for sampling each of theantenna element signals (x₁, x₂, . . . , x_(N)) and processing thesamples of at least some of said antenna signals to form a plurality ofbasis signals (y₀, . . . , y_(M)) together having fewer space-timedimensions than the space-time dimensions of the combined antennasignals, and a plurality of signal processing units each having aplurality of inputs coupled to the common preprocessing section forreceiving all of the basis signals, each processing unit processing andcombining said basis signals to produce a respective one of a set ofestimated received signals (z₀, . . . , z_(M)) each for a correspondingdesired one of the users, the common preprocessing section comprisingfiltering means for combining all of the antenna signals (x₁, x₂, . . ., x_(N))to provide said plurality of basis signals (y₀, . . . , y_(M)),each of the basis signals comprising a different combination of theantenna signals, each of the signal processing units combining the basissignals to provide a user-specific output signal, and updating means forperiodically updating parameters of the filtering means used forderiving each particular basis signal such that each user-specificoutput signal will exhibit a desired optimized concentration of energyof that desired user's received signal as received by the array antenna.2. A receiver according to claim 1, wherein the updating means comprisesmeans for adjusting said parameters in dependence upon channelcharacteristics of all user channels.
 3. A receiver according to claim1, wherein each of the processor units comprises means for weighting thebasis signals (y₀, . . . , y_(M)) before combining same, the weights(w₀₀, . . . , w_(MM)) being adjusted in dependence upon channelcharacteristics of all user channels, and the parameters of thefiltering means are updated less frequently than the weights (w₀₀, . . ., w_(MM)) of the processor units.
 4. A receiver according to claim 1,wherein the number of basis signals is equal to the number of desireduser signals.
 5. A receiver according to claim 1, wherein the commonpreprocessing section comprises M+1 dominant subspace filters producinga set of basis signals y_(m)=[y_(m,1), . . . , y_(m,μ)] where m is theindex of the filter, and m=0, 1, . . . , M, said basis signals y_(m)being projections of the input signals (x₁₁, x₁₂, . . . , x_(1L), x₂₁,x₂₂, . . . , x_(2L), . . . , x_(N1), x_(N2), . . . , x_(NL)) onto the μdimensions of the subspace occupied by signal m which carry the mostenergy.
 6. A receiver according to claim 2, wherein the updating meanscomprises a training sequence generator for generating a trainingsequence for the corresponding user, covariance matrix estimation meansresponsive to the training sequence and the antenna signals forproviding a covariance matrix embodying long-term statistics for thechannel of that user, and eigenvector estimation means for extractingfrom said covariance matrix at least the dominant eigenvectorconstituting said linear combination, elements of said dominanteigenvector being applied to said filtering means as weights forupdating said parameters.
 7. A receiver according to claim 1, whereinthe filtering means comprises a plurality of filters each comprising afilter matched to a respective one of the desired users.
 8. A receiverfor receiving signals from a plurality of transmitting users via anarray antenna having an array of N antenna elements providing a set ofantenna signals (x₁, x₂, . . . x_(N)), respectively, each comprisinginformation from each user, said receiver comprising a commonpreprocessing section followed by a plurality of receiver sections, eachcorresponding to a different one of the users and coupled to the outputsof the common preprocessing section, the preprocessing section samplingeach of the antenna signals (x₁, x₂, . . . , x_(N)) and processing thesamples of at least some of said antenna element signals to form aplurality of basis signals (y₀, . . . , y_(M)) together having fewerspace-time dimensions than the space-time dimensions of the combinedantenna signals, and a plurality of signal processing units each havinga plurality of inputs coupled to the common preprocessing section forreceiving all of the basis signals, each processing unit processing andcombining said basis signals to produce a respective one of a set ofestimated received signals (z₀, . . . , z_(M)) each for a correspondingdesired one of the users, the common preprocessing section comprising(i) means for maintaining through periodic updates a set of dominantsubspace filters, each of which being matched to one of the users ofinterest, and the outputs of which being used by the subsequent receiversections, to be processed and combined in order to yield an estimate ofthe desired signal for each user of interest; (ii) means forperiodically estimating and/or updating the component weights of thedominant subspace filters by correlation, with a known training sequenceor with the user's spreading code in a CDMA system or with any othersignal strongly correlated with the user of interest's signal, incombination with appropriate temporal averaging to isolatesubspace-level information, as opposed to instantaneous channelcharacteristics; and (iii) means for periodically or dynamicallyestimating and/or updating the component weights and/or any otherparameters of interest of the receiver sections fed from thepreprocessing section in a manner and at a rate such that instantaneouschannel changes are tracked to provide a reliable and consistentestimate of the desired signal.
 9. An array receiver system comprisingan array antenna comprising a plurality of antenna elements incombination with an array receiver for processing signals received froma plurality of transmitting users via said array antenna, said arrayantenna having N antenna elements for providing a set of antenna signals(x₁, x₂, x_(N)), respectively, each comprising information from eachuser, wherein said receiver has a common preprocessing section forsampling each of the antenna element signals (x₁, x₂, . . . , x_(N)) andprocessing the samples of at least some of said antenna signals to forma plurality of basis signals (y₀, . . . , y_(M)) together having fewerspace-time dimensions than the space-time dimensions of the combinedantenna signals, and plurality of signal processing units each having aplurality of inputs coupled to the common preprocessing section forreceiving all of the basis signals, each processing unit processing andcombining said basis signals to produce a respective one of a set ofestimated received signals (z₀, . . . , z_(M)) each for a correspondingdesired one of the users, the common preprocessing section comprisingfiltering means for combining all of the antenna signals (x₁, x₂, . . ., x_(N)) to provide said plurality of basis signals (y₀, . . . , y_(M)),each of the basis signals comprising a different combination of theantenna signals, each of the signal processing units combining the basissignals to provide a user-specific output signal, and updating means forperiodically updating parameters of the filtering means used forderiving each particular basis signal such that each user-specificoutput signal will exhibit a desired optimized concentration of energyof that desired user's received signal as received by the array antenna.10. A method of receiving signals from a plurality of transmitting usersvia an array antenna having N antenna elements providing a set ofantenna signals (x₁, x₂, . . . , x_(N)), respectively, each comprisinginformation from each user, the method comprising the steps of: samplingeach of the antenna signals; preprocessing the samples of at least someof said antenna element signals (x₁, x₂, . . . , x_(N)) to form aplurality of basis signals (y₀, . . . , y_(M)) together having fewerspace-time dimensions than the space-time dimensions of the combinedantenna signals, processing and combining said basis signals (y₀, . . ., y_(M)) to produce a set of estimated received signals (z₀, . . . ,z_(M)) each for a corresponding one of the users, the preprocessingincluding the steps of combining all of the antenna signals (x₁, x₂, . .. , x_(N)) to provide said plurality of basis signals (y₀, . . . ,y_(M)) such that each of the basis signals comprises a differentcombination of the antenna signals, the processing and combining stepcomprising the step of combining the basis signals (y₀, . . . , y_(M))to provide a series of user-specific output signals, the method furthercomprising the step of periodically updating parameters used forderiving each particular basis signal such that each user-specificoutput signal will exhibit a desired optimum concentration of energy ofthe received signal if that particular user as received by the arrayantenna.
 11. A method according to claim 10, wherein the updating stepadjusts said parameters in dependence upon channel characteristics ofall user channels.
 12. A method according to claim 10, wherein theupdating step adjusts said parameters in dependence upon channelcharacteristics of all user channels, each step of processing the basissignals weights the basis signals before combining same, and adjusts theweights in dependence upon channel characteristics of all user channels,and wherein the parameters are updated less frequently than the weights.13. A method according to claim 10, wherein the number of basis signalsis equal to the number of desired user signals.
 14. A method accordingto claim 10, wherein the step of preprocessing the samples uses M+1dominant subspace filters to produce a set of basis signalsy_(m)=[y_(m,1), . . . , y_(m,μ)] where m is the index of the filter, andm=0, 1, . . . , M, said basis signals y_(m) being projections of theinput signals (x₁₁, x₁₂, . . . , x_(1L), x₂₁, x₂₂, . . . , x_(2L), . . ., x_(N1), x_(N2), . . . , x_(NL)) onto the μ dimensions of the subspaceoccupied by signal m which carry the most energy.
 15. A method accordingclaim 10, wherein the step of generating a training sequence for eachuser, and wherein: the updating step, responsive to the trainingsequence and the antenna signals, provides a covariance matrix embodyinglong-term statistics for the channel of that user, and uses eigenvectorestimation means for extracting from said covariance matrix at least thedominant eigenvector, elements of said dominant eigenvector beingemployed for updating said parameters.
 16. A method according to claim10, wherein the step of combining all of the antenna signals uses aplurality of filters each matched to a respective one of the desiredusers.
 17. A method of receiving signals from a plurality oftransmitting users using an array antenna having an array of antennaelements and a receiver comprised of a common prefiltering sectionfollowed by a plurality of receiver sections, each corresponding to adifferent one of the users and coupled to the outputs of the commonprefiltering section, the method comprising the steps of (i) maintainingthrough periodic updates a set of dominant subspace filters, eachmatched to one of the users of interest, and the outputs of which beingused by the subsequent receiver sections, to be processed and combinedin order to yield an estimate of the desired signal for each user ofinterest; (ii) periodically estimating and/or updating the componentweights of the dominant subspace filters by correlation with at leastone of (a) a known training sequence, (b) the user's spreading codewhere the method is used in a CDMA system, and (c) any other signalstrongly correlated with the signal of the user of interest, incombination with appropriate temporal averaging to isolatesubspace-level information, as opposed to instantaneous channelcharacteristics; and (iii) periodically or dynamically estimating and/orupdating the component weights and/or any other parameters of interestof the receiver sections fed from the prefiltering section in a mannerand at a rate such that instantaneous channel changes are tracked toprovide a reliable and consistent estimate of the desired signal.